Revisiting Private Stream Aggregation: Lattice-Based PSA
Daniela Becker, Jorge Guajardo, Karl-Heinz Zimmermann · 2018
In this age of massive data gathering for purposes of personalization, targeted ads, etc. there is an increased need for technology that allows for data analysis in a privacy-preserving manner.Private Stream Aggregation as introduced by Shi et al. (NDSS 2011) allows for the execution of aggregation operations over privacy-critical data from multiple data sources without placing trust in the aggregator and while maintaining differential privacy guarantees.We propose a generic PSA scheme, LaPS, based on the Learning With Error problem, which allows for a flexible choice of the utilized privacy-preserving mechanism while maintaining post-quantum security.We overcome the limitations of earlier schemes by relaxing previous assumptions in the security model and provide an efficient and compact scheme with high scalability.Our scheme is practical, for a plaintext space of 2 16 and 1000 participants we achieve a performance gain in decryption of roughly 150 times compared to previous results in Shi et al. (NDSS 2011).